RESUMEN

Mortality is one of the most important epidemiological measures and a key indicator of the effectiveness of potential treatments or interventions. In this paper, a permutation test method of variance analysis is proposed to test the null hypothesis that the real-time fatality rates of multiple groups were equal during the epidemic period. In light of large-scale simulation studies, the proposed test method can accurately identify the differences between different groups and display satisfactory performance. We apply the proposed method to the real dataset of the COVID-19 epidemic in mainland China (excluding Hubei), Hubei Province (excluding Wuhan), and Wuhan from 31 January 2020 to 30 March 2020. By comparing the differences in the disease severity for differential cities, we show that the severity of the early disease of COVID-19 may be related to the effectiveness of interventions and the improvement in medical resources.

RESUMEN

Voids behind a lining may develop due to insufficient backfilling, poor workmanship, water erosion or gravity. They affect the interaction between the surrounding rock and lining and even cause instability of the lining structure. To ensure the safe operation of tunnels, it is very important to study the influence of voids behind the lining of the lining structure. In this paper, a laboratory model of a tunnel lining was established by taking the voids behind the lining of the Wushan Tunnel as an example. By changing the position and size of the voids, the corresponding stress variation law of the lining was obtained, and the influence of the voids behind the lining on the structural stability of the highway tunnel was analyzed. The experimental results showed that the voids behind the lining led to an increase in the stress near the voids, especially the voids at the vault. The circumferential stress and axial stress increased with increasing void depth and length, and the increase was greater with increasing void depth than increasing length; that is, the void depth had a greater effect on the lining stress. When the vault void depth was 30 mm, the axial tensile stress of the vault was 0.281 MPa, and the maximum increase was 178.2% compared with that without voids. The safety factors at different lining positions, from large to small, are: arch foot > spinner > arch top > arch waist. In the processes of lining operation and maintenance, special attention should be given to the treatment of voids behind the lining, especially deep voids.

RESUMEN

Ground blast loads are of great importance to the safe operation of steel and gas pipelines, and the results obtained from traditional theoretical formulas for pipeline safety prediction are in error with the actual measured data. In this paper, full-size field tests and corresponding numerical simulations are carried out using Timoshenko beam theory and explosion stress wave theory, which consider shear effects. At the same time, combined with the theory of foundation stiffness and pipeline stiffness flexibility ratio, a modified theoretical model is obtained in line with the actual conditions of the site, which can accurately calculate the deformation and displacement of pipeline underground explosion load, and greatly reduce the error of theoretical prediction results. The innovation of the research results in this paper is that the theoretical stress in the Timoshenko beam can be replaced by the circumferential strain. On the other hand, the modified theoretical solution can obtain the critical weight of explosives to prevent pipeline damage at different buried depths. It provides a theoretical basis for the protection of pipelines' underground blast loads and provides research ideas for the safe protection and design of pipelines.

RESUMEN

We propose a mechanical learning method that can be used to predict stability coefficients for slopes where slopes with predetermined shear planes are subjected to cyclic seismic loads under undrained conditions. Firstly, shear tests with cyclic loading of different parameters were simulated on designated slip zone soil specimens, in which the strain softening process leading to landslide occurrence was closely observed. At the same time, based on the limit equilibrium analysis of the Sarma method, the variation of slope stability coefficients under different cyclic loads was investigated. Finally, a Box-Jenkins' modeling approach is used to predict the data from the time series of slope stability coefficients using a mechanical learning approach. The simulation results show that (1) reduction in coordination number can be an accurate indicator of the level of strain softening and evolutionary processes; (2) the gradual reduction of shear stress facilitates the soil strain softening process, while different cyclic loading stress amplitudes will result in rapid penetration or non-penetration of the fracture zone by means of particulate flow. Although the confining pressure of the slip zone soil can inhibit the increase of fractures, it has a limited inhibitory effect on strain softening; (3) based on field observations of the slope stability factor and stress field, two possible landslide triggering mechanisms are described. (4) Mechanical learning of time series can accurately predict the changing pattern of stability coefficients of slopes without loading. This study establishes a potential bridge between the geological investigation of landslides and the theoretical background of landslide stability coefficient prediction.

RESUMEN

Most deep learning-based action recognition models focus only on short-term motions, so the model often causes misjudgments of actions that are combined by multiple processes, such as long jump, high jump, etc. The proposal of Temporal Segment Networks (TSN) enables the network to capture long-term information in the video, but ignores that some unrelated frames or areas in the video can also cause great interference to action recognition. To solve this problem, a soft attention mechanism is introduced in TSN and a Spatial-Temporal Attention Temporal Segment Networks (STA-TSN), which retains the ability to capture long-term information and enables the network to adaptively focus on key features in space and time, is proposed. First, a multi-scale spatial focus feature enhancement strategy is proposed to fuse original convolution features with multi-scale spatial focus features obtained through a soft attention mechanism with spatial pyramid pooling. Second, a deep learning-based key frames exploration module, which utilizes a soft attention mechanism based on Long-Short Term Memory (LSTM) to adaptively learn temporal attention weights, is designed. Third, a temporal-attention regularization is developed to guide our STA-TSN to better realize the exploration of key frames. Finally, the experimental results show that our proposed STA-TSN outperforms TSN in the four public datasets UCF101, HMDB51, JHMDB and THUMOS14, as well as achieves state-of-the-art results.

Asunto(s)

Medios de Comunicación , Redes Neurales de la Computación , Memoria a Largo Plazo , Reconocimiento en Psicología

RESUMEN

An innovative test equipment for analyzing the dynamic damage at the interface of geosynthetic materials is introduced. It provides a result of shear damage on the structural surface of a geosynthetic material that has been subjected to cyclic loading of different amplitudes. First, the experimental data of geosynthetics under cyclic loading are obtained based on the innovative test equipment mentioned above, and a mathematical identification method for the relationship between cyclic loading parameters and shear mechanical behavior of geosynthetics is proposed based on the Mohr-Coulomb criterion, assuming that the micro-element strength of geosynthetics follows Weibull distribution. Moreover, the proposed model, which takes into account repeated loading-unloading conditions, enables this simulated damage process to have properties of reoccurring load cycles, and the proposed method was evaluated by experimental datasets from this study and the existing literature and proved to have a satisfying performance in predicting mechanical behavior of shear stress in geosynthetics under different cyclic loadings. Second, the self-developed test instrument can well solve the direct shear parameters of geosynthetics under dynamic load and can be obtained as a new dynamic statistical model with rate-dependence in interface damage.